Identification of Soil Quality Factors and Indicators in Mediterranean Agro-Ecosystems

Abstract

Soil quality offers a holistic approach for understanding the relationships between soil’s biological, chemical, and physical properties, which is crucial for sustainable land use and the management of non-renewable soil resources. This study evaluates the impact of land use on a set of 23 soil quality indicators (SQIs) across 5 land uses of the Mediterranean agro-ecosystems: forest, olive groves, wheat fields, a corn/wheat crop rotation system, and pasture. Seasonal soil sampling was carried out over two consecutive years in three conventionally managed fields representing each land use type. For each sampling, physicals SQIs (soil moisture, porosity-Vp-, bulck density-BD-, water holding capacity-WHC-, clay, silt, sand), chemical SQIs (organic carbon-Corg-, total Nitrogen-TN-, C/N, PH, electrical conductivity-EC-, ammonium-NH₄-N-, nitrate-NO₃-N- and available nitrogen-Nmin-), and biological SQIs (soil microbial biomass C-Cmic- and N-Nmic-, Cmic/Nmic, Cmic/Corg, Nmic/TN, active carbon—Cact-, Cact/Corg) were evaluated. Through multivariate analysis, five key soil quality factors—organic matter, microbial biomass, nutrients, C/N ratio, and compaction—were identified as indicators of soil quality changes due to land use, explaining 82.9% of the total variability in the data. Discriminant analysis identified organic matter and the C/N factors as particularly sensitive indicators of soil quality changes, reflecting the quantity and quality of soil organic matter, incorporating 87.8% of the SQIs information resulting from the 23 indicators. ΤΝ, accounting for 84% of the information on the organic matter factor, emerges as a key indicator for predicting significant changes in soil quality due to land use or management practices. The TN and C/N proposed indicators offer a simplified yet effective means of assessing soil resource sustainability in the Mediterranean agroecosystems, providing practical tools for monitoring and managing soil quality.

1. Introduction

The concept of soil quality was initially defined as “the capacity of soil to function within an ecosystem and under various land uses in such a way that it sustains biological productivity, maintains water and air quality, and promotes the health of animals and plants” [1]. Larson and Pierce [2] expanded on this by suggesting that a soil’s physical, chemical, and biological characteristics enable it to perform three essential functions: (1) provide a medium for plant growth, (2) control and regulate water flow in the environment, and (3) serve as an environmental filter.

While the concept of soil quality may seem straightforward, its definition and quantification pose considerable challenges [3]. Some researchers contend that the term “quality” is difficult to apply to soil, given its dynamic, complex, and variable nature [4,5,6]. However, a growing number of studies underscore the critical role of soil quality in environmental sustainability and human well-being [7,8,9]. Soil quality provides a comprehensive framework for examining the interactions among the biological, chemical, and physical properties of soil, which is essential for sustainable land use and effective soil management of non-renewable soil resources [1,3,4,5,6,7,8,9,10,11]. For this reason, Lal [12] recently suggested that restoring soil quality in agricultural lands could help mitigate soil degradation.

Evaluating soil quality requires an analysis of both the inherent and dynamic characteristics of soil. In any region, soil quality assessment is influenced by a combination of factors, including management practices like crop rotation and manure application, as well as climate and soil type [9]. The initial step in assessing soil quality involves identifying suitable soil quality indicators (SQIs) to create a minimum dataset (MDS) for assessment [13]. Selecting indicators that encompass a wide range of physical, chemical, and biological attributes is crucial for an accurate evaluation of soil quality. Additionally, it is important to ensure that the chosen parameters effectively convey the information offered by all relevant indicators [14].

Many soil characteristics that influence soil quality are often highly correlated, interacting with other soil properties [2,11,14]. Due to these correlations, a more robust evaluation of soil quality can be achieved using statistical approaches that take these relationships into account. Multivariate statistical analyses, for example, allow for the examination of multiple correlated variables at once, revealing patterns that might be missed when variables are assessed independently [15]. Numerous studies have utilized multivariate methods to identify a smaller set of soil quality indicators, an MDS that can effectively describe changes in soil quality [16,17,18,19], and these have been applied to various land use types, including coastal areas [20], agricultural zones [21], and grasslands [19]. Zhou et al. [22] used ANOVA and factor analysis to identify a subset of 4 key soil indicators from an initial group of 26 to build an MDS for evaluating soil quality in wheat-producing regions of China. Similarly, Brejda et al. [21] employed principal component and discriminant analyses to identify sensitive soil indicators at a regional scale.

In the Mediterranean region, only a few studies have developed specific sets of soil quality indicators for specific land uses like forest [23,24] and agricultural land uses e.g., [10,11,25,26,27], and even fewer have incorporated biological parameters [10,11,26]. Reis and Dintaroglou [28] employed Principal Component Analysis to evaluate dynamic soil quality in a semi-arid Mediterranean watershed for different land uses. Navaro et al. [29] used multivariate methods to select the most appropriate indicators for a soil quality index in Mediterranean ecosystems.

Most studies aimed at identifying MDS do not account for the seasonal variability of soil quality indicators, as soil sampling is typically conducted during one specific season. Soil function is significantly influenced by seasonal variations in temperature and moisture, as well as by management practices in agricultural systems. This is particularly important in Mediterranean regions, which are characterized by a pronounced seasonal contrast in temperature and rainfall between winter and summer. Soil quality indicators in Mediterranean agroecosystems often exhibit significant seasonal variability that is frequently overlooked in efforts to establish MDS at a regional scale [30,31]. Furthermore, Mediterranean agroecosystems are notable for their highly variable soil cover, spatial diversity, and long history of continuous human settlement and intensive cultivation [32], which further influence soil quality.

To address these challenges, the present study introduces a statistics-based methodology for identifying an MDS for soil quality assessment, incorporating seasonal samplings of soils, across five different land use types, over two consecutive years. The study aimed to: (i) identify regional-scale soil quality “factors” from a set of 23 biological, chemical, and physical soil quality indicators, (ii) determine which soil quality factors exhibit significant variation based on land use, and (iii) pinpoint soil properties that can serve as reliable indicators for monitoring soil quality on a regional scale, taking into account the seasonal variation of soil functions in Mediterranean agroecosystems.

2. Materials and Methods

2.1. Study Area

The study was conducted in the Kaloni Gulf watershed, located on Lesvos Island (Longitude: 26°06′51 E; Latitude: 39°12′40 N), in the northern Aegean Sea (Figure 1). Lesvos, which is the third-largest Greek island and the seventh-largest in the Mediterranean, features a diverse range of lithological units, climates, and landscapes, including forests, scrubland, and agricultural land, with olive cultivation being predominant.

Lesvos is representative of the land-use changes that have occurred in the Mediterranean region over the past century [33]. Studies combining vegetation cover and soil geology have highlighted the complex interactions between climate, land use, and land degradation on the island [34,35,36]. These long-term interactions between climate, soil, and human activities have had varying impacts on land degradation and desertification. Significant land-use changes have occurred in the study area throughout the 20th century. The area dedicated to olive cultivation has expanded considerably, increasing from 26.9% of the island’s total area in 1886 to 41.2% today and has been abandoned in areas of marginal productivity and moved to more fertile regions. The extent of pine forests has remained relatively constant, though their geographic distribution has shifted [33]. Pine forests have replaced some oak woodlands and grasslands due to their greater resilience and ability to regenerate after fire, as well as their capacity to thrive in shallow soils with alkaline parent material, which are less suitable for oak trees. The area under cereal cultivation has significantly declined, particularly after 1950, due to extensive migration of the local population to urban centers [37] and decreasing soil fertility [33]. Animal husbandry remains the second largest sector in primary production on the island, characterized by traditional Mediterranean practices. Extensive sheep and, to a lesser extent, goat farming is prevalent. In recent decades, grazing intensity has increased sharply due to (a) a significant reduction in grain cultivation and (b) a substantial rise in subsidies for grazing livestock. The intensity of grazing is often exacerbated by the annual setting of fires in rangelands to encourage the growth of high-quality grass, which is then subject to overgrazing. This overgrazing has led to increased soil erosion and has significantly impacted the water balance in these areas [34].

The study area is characterized by a Mediterranean climate, known for its mild, rainy winters and hot, dry summers, with a long dry season and abundant sunshine, particularly in the summer months. A notable feature of this climate is the pronounced seasonal variation in key climatic parameters: temperatures peak during the summer, while precipitation reaches its highest levels in winter. The area’s climate is also influenced by the sea and the dominance of northerly winds during the summer months. The average annual temperature is 19.1 °C, and the annual precipitation averages 608.1 mm, with rainfall peaking in December and reaching its lowest levels in July or August [38]. Relative humidity follows a similar pattern, with the highest levels in December and January (72–71%) and the lowest in July (55.9%), resulting in an average annual humidity of 64.4%. The prevailing winds are predominantly from the north and northwest, occurring frequently during both winter (with the highest frequency of moderate and strong winds in February) and summer. May is typically the calmest month, experiencing the lowest wind activity [38]. Rainfall and temperature fluctuations over the two-year study period are presented in Figure 2.

The watershed covers a total area of 49,260 hectares, comprising 33.5% cropland, 39.4% pasture, 21.6% forest, and 5.5% allocated to other uses. Olive cultivation dominates the cropland, representing 70% of the area, while the remaining land is primarily used for arable crops such as wheat (Mandylas, 1998) [38]. The region’s soils originate from Mio-Pliocene volcanic pyroclastic deposits, with consistent soil-forming factors observed across agricultural lands surrounding the Kalloni Gulf.

Five land covers representing forest, cropland, and pasture were selected to reflect the island’s ecosystem diversity: pine forests Pinus brutia Ten, olive groves, wheat fields, crop rotations of corn-wheat (referred to as double cultivation), and shrubland pastures. In a preliminary screening analysis, soil samples were collected from 75 representative fields (20 pastures, 15 forests, 40 arable) covering all land use/cover types, with slopes of less than 3% to minimize erosion effects. Three fields (three hectares each) for each land cover were selected after cluster analysis to ensure similar soil texture of 23 to 30% clay and a pH 6–7. All selected soils were classified according to USDA soil taxonomy as Entisols, with Typic Xerofluvents for forest, olive trees, wheat, and wheat/maize double cultivation soils, and Lithic Xerorthents for pastures. All soil textures were sandy-clay-loam.

Soil samples were collected from conventionally farmed fields according to local practices. Forest areas have undergone minimal management, with resin collection activities discontinued approximately 30 years ago. Olive grove sites were tilled to a depth of 15 cm in April to incorporate annual vegetation and minimize water competition. Composite 15%N—15%P₂O₅—15%K₂O fertilizer was applied every three years to each tree (45 kg N ha⁻¹). Soil samples in olive grove fields were taken the second and third year after fertilization. In wheat cultivation, seedbeds were prepared at the end of October by using a moldboard plow to turn the soil to a depth of 20–30 cm, incorporating the previous crop plant residues. No preplant fertilization was applied, and in-season ammonium nitrate (NH₄NO₃) was applied at a rate of 90–100 kg N ha⁻¹ in February. Wheat is harvested in June, and fields remain bare until the next seeding in autumn. Crop rotation was implemented in a limited area for forage production, where cereals were planted in October following deep tillage using a moldboard plow to a depth of 40–50 cm. Pre-plant fertilization with 11%N—15%P₂O₅—15%K₂O fertilizer was applied (110 kg N ha⁻¹), and no in-season fertilization was performed, as harvest occurred in late April. A second deep tillage was performed between May 10th and 20th before maize seeding, with pre-plant fertilization of 110 kg ha⁻¹ applied using composite 11%N—15%P₂O₅—15%K₂O fertilizer. Maize cultivation was irrigated using a drip system at intervals of 8–10 days, spanning from late June to the end of August, with a total water application ranging from 550 to 600 mm. In-season N fertilization was performed through irrigation water (fertigation) 3–4 times, totaling 165 kg N ha⁻¹ as NH₄NO₃. Maize was harvested in late September. All management practices in olives, wheats, and double cultivation are consistently applied between years. Pasture sites, dominated by Sarcopoterium spinosum (L.) Spach, had shallow soils in a region with severe degradation through erosion. These soils did not receive any particular management except for grazing by sheep and goats.

2.2. Soil Sampling and Analysis

Over two consecutive years, soil sampling was conducted eight times, seasonally, on the same day after the last rainy day for all land uses, and when soil has been drained enough. Soil sampling was performed every year in the first days of May, August, October, and February. At each sampling site, three composite surface soil samples (0–15 cm depth) were collected randomly from the central area of the site, with each composite sample comprising 8 soil cores (2.5 cm diameter). Prior to sampling, all vegetation and plant residues were cleared from soil surface. The soil sub-samples were carefully combined to achieve homogeneity, with roots and any visible plant residues removed. The resulting composite samples were then stored at 4 °C for three days before soil microbial biomass determinations. Furthermore, soil subsamples were air-dried, ground, sieved through a 2 mm mesh, and stored separately for subsequent chemical analysis.

2.3. Soil Quality Indicators

A set of physical, chemical, and biological indicators of soil quality, originally proposed with the introduction of the soil quality concept [1,2] and still widely regarded by researchers today as critical for achieving key soil functions [39], were selected. Additionally, and particularly for the Mediterranean area where seasonal change affects soil function, indicators that have been used to monitor soil quality and are sensitive to seasonal changes, such as soil moisture, soil microbial properties, and the availability of soil nutrients, were selected.

The selected physical, chemical, and biological soil properties included in this study are well described by Brandy and Weill [40] for their importance on soil function. More specifically, the physical SQIs selected include: soil moisture, water holding capacity (WHC), soil porosity (Vp), and bulk density (BD), which influence aeration and water movement in the soil. Additionally, particle size distribution (Clay%, Silt%, Sand%) was assessed, as it is a fundamental property affecting various processes, such as moisture and nutrient retention. The chemical soil quality indicators monitored were soil organic carbon (Corg) and total nitrogen (TN), both key components of soil organic matter that influence a variety of chemical and biological processes in the soil. The C/N ratio was also included as it serves as an indicator of organic matter quality and its mineralization rate. Other chemical indicators included the availability of essential nutrients (phosphorus (P), nitrogen (NO₃_N, NH₄_N and mineral N Nmin), and potassium (K)) as well as soil pH and electrical conductivity (EC). Biological indicators focused on soil microbial biomass carbon (Cmic) and nitrogen (Nmic), as well as active carbon (Cact), measured as permanganate oxidizable carbon (POXC), a well-established indicator of soil health in agriculture, known for its sensitivity to changes in conditions or management practices [41]. Additionally, the ratios of Cmic/Corg, Nmic/TN, Cmic/Nmic, and Cact/Corg were examined.

2.4. Laboratory Analyses

Soil moisture (gravimetric water content) was determined by drying triplicate 10 g samples at 105 °C. Bulk density was estimated using the Core method (volumetric cylinder method), after samplings of undisturbed soil samples by a specific cylinder, and estimating the soil mass in the cylinder. Maximum water holding capacity (WHC) was measured using the Gardner, 1986 method [42], where each soil sample was saturated with water in a cylinder, and WHC was calculated based on the water weight held in the sample vs. the sample dry mass (dried at 105 °C for 24 h). Soil texture was determined by physical fractionation (particle-size analysis, PSA) using the Bouyoucos method after the destruction of organic matter with hydrogen peroxide and dispersion with sodium hexametaphosphate [43]. Soil organic C was estimated by the Walkley–Black procedure [44] and total N by the semimicro-Kjeldahl method [45]. Nitrate and ammonium nitrogen were estimated chromatographically using the “cadmium reduction” and “indophenol blue method”, respectively [46]. Soil-available phosphorus was extracted using the method recommended by Olsen and Sommers [47] and quantified through spectrophotometric analysis. Soil pH and electrical conductivity (EC) were measured in a 1:1 suspension with water [48]. Microbial biomass carbon (Cmic) and nitrogen (Nmic) were determined using the fumigation-extraction method developed by Vance, Brookes, and Jenkinson [49] for Cmic, and Brookes, Landman, and Jenkinson [50] for Nmic. Active carbon (Cact) was estimated using the permanganate-oxidizable carbon method [41]. All data are expressed on an oven-dry (at 105 °C) soil weight basis.

2.5. Statistical Analysis

The statistical analysis was conducted using the SPSS statistical software–(IBM SPSS Statistics 26.0). To assess differences in physical, chemical, and biological soil quality indicators across land uses, analysis of variance (ANOVA) was employed. The identification of samples with significant statistical differences was carried out using the LSD post hoc test for multiple comparisons. The Pearson correlation test was applied to assess the relationships between SQIs across all land uses. Before conducting these analyses, the data were checked for compliance with ANOVA assumptions and log-transformed where necessary. Principal component analysis (PCA) was employed to reduce the set of 23 soil quality indicators to a smaller number of factors, highlighting the most influential properties that explain the variation in the data. PCA was used to extract the factors, as it does not require prior assessment of the variance of each soil property explained by the factors [21]. PCA was performed on standardized variables using the correlation matrix to neutralize the effects of different measurement units on determining the weight of each factor loading [15,51]. Factors with eigenvalues greater than 1 were selected from the analysis using “varimax” rotation. Discriminant analysis (DA) was applied to the complete set of physical, chemical, and biological soil quality indicators, considering the impacts of land use and season as recorded by the eight seasonal samplings per land use. DA was employed to differentiate land uses based on their physical, chemical, and biological soil quality indicators, examine their spatial relationships, and identify key properties that predominantly affect this distinction.

3. Results

3.1. Land Use Effect on Soil Quality Indicators

The results of the study highlight the impact of land use on various soil quality indicators, taking into account the specific conditions associated with each land use type. Agricultural activities significantly modify soil parameters when compared to natural ecosystems like forests. Notable differences were also observed within agricultural land uses. Soil cultivation appears to have a consistent impact on the studied parameters, grouping the three crop types—olive groves, wheats, and double cultivation—together. This indicates that soil cultivation has a distinct influence on soil functions, setting it apart from other forms of agricultural use, such as pastures.

Table 1. Soil quality indicators for each land use. Average values of the eight soil samplings.

	Soil Quality Indicator	Forest	Olive Trees	Wheats	Double Cultivation	Pasture
Physical SQIs	BD, g/cm3	1.25 c	1.39 b	1.21 c	1.49 a	1.40 b
	Vp, %	52.65 a	47.67 b	54.38 a	43.64 c	47.00 b
	Soil mois., %	19.28 a	11.07 c	13.44 bc	14.67 b	12.89 bc
	WHC, %	79.09 a	49.13 c	60.29 b	56.99 b	51.93 c
	Clay, %	30.85 a	22.96 b	26.86 a	24.42 a	29.87 a
	Silt, %	23.88 ab	24.48 ab	24.92	27.47 a	21.43 b
	Sand, %	45.27 b	52.56 a	48.22 a	48.11 a	48.71 a
Chemical SQIs	Corg, g kg−1	17.49 a	9.68 d	10.55 c	8.77 e	13.44 b
	TN, g kg−1	1.14 a	0.76 c	0.97 b	0.78 c	0.95 b
	C/N	15.64 a	13.08 bc	11.81 c	11.60 c	14.31 ab
	NO3_N, mg kg−1	3.75 b	6.53 b	19.52 a	20.75 a	20.59 a
	NH4_N, mg kg−1	8.75 a	2.66 c	5.68 b	5.32 b	9.49 a
	Nmin, mg kg−1	12.50 c	9.19 c	25.21 b	26.07 ab	30.07 a
	P, mg kg−1	2.93 d	20.76 b	24.17 a	24.74 a	6.45 c
	EC, dS m−1	0.40 a	0.19 b	0.39 a	0.45 a	0.38 a
	PH	7.00 a	6.17 b	6.13 b	6.27 b	6.03 b
Biological SQIs	Cmic, mg kg−1	332.26 a	164.31 c	271.57 b	159.26 c	360.13 a
	Cmic/Corg, %	1.90 b	1.70 b	2.64 a	1.85 b	2.70 a
	Nmic, mg kg−1	49.95 b	24.48 c	53.28 b	23.31 c	69.62 a
	Nmic/Ntot, %	4.44 c	3.34 cd	5.68 b	3.13 d	7.29 a
	Cmic/Nmic	8.01 a	7.02 ab	6.35 ab	7.16 ab	5.78 b
	Cact, mg kg−1	411.27 a	291.64 bc	331.57 b	265.55 c	374.23 a
	Cact/Corg, %	2.36 b	3.03 a	3.15 a	3.07 a	2.81 a

Values for each indicator with the same letter does not differ significantly for p < 0.05 according to LSD post hoc test.

provides all samplings average values for SQIs measured, revealing distinct patterns in soil characteristics. For example, forest shows the highest values for soil moisture content, Vp, WHC, Corg, Cmic, and Cact. Pasture soils similarly exhibit high values for Cmic and Cact but are particularly notable for the highest levels of Nmic, Nmic/Ntot ratio, and Nmin, as well as a high Cmic/Corg ratio, similar to wheat soils. Wheat soils also demonstrate high Vp and Cact/Corg ratios, while double-cropping systems show elevated values for electrical conductivity EC, NO₃-N, NH₄-N, P, and BD.

Seasonal variations in SQIs were substantial, driven by the Mediterranean climate’s fluctuations in temperature and rainfall, as well as agricultural practices such as irrigation, fertilization, and tillage. Of the 23 SQIs studied, significant seasonal variability in at least one land-use type was observed in the following 13 indices: soil moisture, NO₃-N, NH₄-N, Nmin, P, EC, Cmic, Nmic, and the ratios of Cmic/Nmic, Cmic/Corg, Nmic/Ntot, Cact, and Cact/Corg. Although the main objective of this study is not to present the seasonal variation of the soil quality indicators, Figure 3 presents the seasonal variation of four selected SQIs that affect soil function.

As shown in Figure 3, soil moisture in all non-irrigated land uses follows a similar pattern, characterized by a notable decrease in summer and a significant increase in autumn with the onset of rainfall. In contrast, the irrigated double crop maintains more stable moisture levels during the summer. The typical hot and dry Mediterranean summer appears to impact soil microbial biomass consistently across all land uses, as the seasonal variation pattern remains similar. Available nitrogen is also affected, not only by fertilization in grain and double cropping but also by organic nitrogen mineralization, which increases significantly in autumn with the start of the rainy season. Additionally, the Cmic/Nmic ratio rises in all non-irrigated land uses during summer and declines with the onset of autumn rains.

Significant correlations between the SQIs found in 189 of 253 pairs as can be seen in

Table 2. Correlation coefficients for soil quality indicators (n = 120).

	BD	Vp	Moist	WHC	Clay	Silt	Sand	Corg	TN	C/N	NO3-N	NH4-N	Nmin	P	EC	pH	Cmic	Cmc/Corg	Nmic	Nmic/Ntot	Cmic/Nmic	Cact
Vp	−1.00 **
moist	−0.13 *	0.13 *
WHC	−0.43 **	0.43 **	0.39 **
clay	−0.35 **	0.35 **	0.28 **	0.48 **
silt	ns	ns	ns	ns	−0.52 **
sand	0.38 **	−0.38 **	−0.32 **	−0.62 **	−0.65 **	−0.31 **
Corg	−0.41 **	0.41 **	0.33 **	0.69 **	0.77 **	−0.37 **	−0.53 **
TN	−0.32 **	0.32 **	0.40 **	0.60 **	0.54 **	−0.29 **	−0.34 **	0.65 **
C/N	−0.15 *	0.15 *	ns	0.13 *	0.29 **	ns	−0.23 **	0.38 **	−0.30 **
NO3-N	ns	ns	ns	−0.13 *	ns	ns	ns	−0.25 **	ns	−0.29 **
NH4-N	ns	ns	0.27 **	0.27 **	0.61 **	−0.30 **	−0.41 **	0.47 **	0.33 **	0.16 *	ns
Nmin	ns	ns	ns	ns	0.15 *	ns	−0.20 **	ns	0.19 **	−0.23 **	0.95 **	0.33 **
P	0.14 *	−0.14 *	−0.19 **	−0.36 **	−0.77 **	0.45 **	0.45 **	−0.78 **	−0.36 **	−0.42 **	0.41 **	−0.47 **	0.24 **
EC	−0.15 *	0.15 *	0.27 **	0.26 **	0.20 **	ns	−0.29 **	ns	0.24 **	ns	0.39 **	0.24 **	0.45 **	ns
pH	−0.27 **	0.27 **	0.25 **	0.52 **	0.29 **	ns	−0.38 **	0.44 **	0.33 **	ns	−0.28 **	ns	−0.23 **	−0.34 **	0.23 **
Cmic	−0.27 **	0.27 **	0.38 **	0.36 **	0.68 **	−0.47 **	−0.33 **	0.60 **	0.59 **	ns	0.16 *	0.57 **	0.33 **	−0.52 **	0.19 **	ns
Cmic/Corg	ns	ns	0.20 **	ns	0.25 **	−0.28 **	ns	ns	0.24 **	−0.16 *	0.36 **	0.31 **	0.43 **	ns	ns	−0.19 **	0.75 **
Nmic	−0.22 **	0.22 **	0.43 **	ns	0.55 **	−0.42 **	−0.23 **	0.39 **	0.45 **	ns	0.28 **	0.51 **	0.43 **	−0.35 **	0.16 *	ns	0.72 **	0.56 **
Nmic/TN	ns	ns	0.27 **	−0.15 *	0.39 **	−0.34 **	−0.13 *	0.16 *	ns	0.35 **	0.22 **	0.42 **	0.34 **	−0.25 **	ns	−0.19 **	0.53 **	0.52 **	0.85 **
Cmic/Nmic	ns	ns	−0.19 **	0.24 **	ns	ns	ns	ns	ns	ns	−0.15 *	−0.14 *	−0.18 **	ns	ns	0.13 *	ns	ns	−0.52 **	−0.53 **
Cact	−0.28 **	0.28 **	0.21 **	0.60 **	0.52 **	−0.29 **	−0.32 **	0.64 **	0.62 **	ns	ns	0.35 **	ns	−0.47 **	ns	0.18 **	0.56 **	0.20 **	0.30 **	ns	0.19 **
Cact/Corg	0.18 **	−0.18 **	−0.13 *	ns	−0.28 **	ns	0.22 **	−0.40 **	ns	−0.37 **	0.25 **	−0.13 *	0.20 **	0.33 **	ns	−0.27 **	ns	0.28 **	−0.16 *	−0.19 **	0.20 **	0.41 **

ns: not significant, * p < 005, ** p < 0.001. Bold text indicates a significant correlation with r > 0.5.

, indicating that SQIs can be grouped into fewer factors, based on their correlation structure. Selected strong correlations (r² > 0.6) indicates Clay% correlations with Sand%, Corg, Cmic, P, and NH₄-N, WHC with Sand, Corg, and TN, and Corg with TN, P, Cmic and Cact.

3.2. Distinction of Land Uses Based on Physical, Chemical, and Biological Soil Quality Indicators

Discriminant analysis (DA) was used to identify the key SQIs that differentiate land uses. Using land use as a grouping parameter, DA produced four significant functions, explaining 100% of the total variability (

Table 3. Standardized coefficients and properties of the discriminant analysis for physical, chemical, and biological SQIs.

Soil Quality Indicator	Function 1	Function 2	Function 3	Function 4
BD	0.511	0.307	−0.353	0.734
moist	−0.146	0.244	−0.486	0.186
WHC	0.356	0.754	−0.073	0.020
clay	0.690	0.595	0.495	0.306
silt	−0.318	0.857	0.229	0.417
Corg	0.511	0.406	−0.638	−0.266
TN	0.281	0.371	0.410	−0.358
C/N	−0.284	0.483	−0.130	0.123
NO3-N	0.397	−0.952	−0.161	0.687
NH4-N	0.402	−0.180	0.081	0.215
P	−1.096	0.767	0.510	−0.118
EC	0.438	0.094	0.074	0.234
pH	−0.161	0.105	−0.224	−0.063
Cmic	−1.053	−0.515	−1.229	0.546
Cmic/Corg	0.849	0.523	1.306	−0.572
Nmic	−0.217	0.491	0.067	0.182
Nmic/Ntot	0.599	−0.584	0.575	−0.656
Cmic/Nmic	0.235	−0.030	0.182	−0.093
Cact	0.474	−1.087	1.581	−0.456
Cact/Corg	−0.786	0.682	−1.442	0.245
Cmic/Nmic	8.01	7.02	−0.353	0.734
Cact	411.27	291.64	−0.486	0.186
Eigenvalues	41.596	5.727	2.362	1.744
Commulative variation %	80.9	92.0	96.6	100.0
Sig.	<0.001	<0.001	<0.001	<0.001

Bold text indicates the most significant coefficient for each function.

).

The first two functions accounted for 80.9% and 11.1% of the variability, respectively, with P, Cmic, Cact, and NO3-N being the most influential properties for land-use differentiation. Figure 4 shows the clear separation of land-use types, with crops grouped distinctly apart from forests and pastures.

While crops showed some overlap, particularly between double cultivation and wheats, the centroids for all land-use types remained distinct (

Table 4. Pairwise comparison of land uses for existence significant difference of their centroids.

Land Use		Forest	Olive Trees	Wheats	Double Cultivation
Forest	F	357.378
	Sig.	0.000
Olive trees	F	207.950	57.095
	Sig.	0.000	0.000
Wheats	F	281.162	36.172	34.197
	Sig.	0.000	0.000	0.000
Double cultivation	F	70.362	260.748	165.884	230.242
	Sig.	0.000	0.000	0.000	0.000

).

3.3. Factor Analysis

Principal component analysis (PCA) revealed six main factors with eigenvalues greater than 1, which explained 82.9% of the total variance of the data (

Table 5. The first 10 factors of principal component analysis.

Component	Initial Eigenvalues
	Total	% of Variance	Cumulative %
1	5.57	29.34	29.34
2	3.46	18.19	47.53
3	2.38	12.51	60.04
4	1.83	9.64	69.68
5	1.36	7.17	76.85
6	1.15	6.04	82.89
7	0.87	4.58	87.47
8	0.73	3.86	91.33
9	0.55	2.90	94.24
10	0.42	2.18	96.42

).

If there were no correlation between soil properties, the identification of factors would not be possible [52]. However, as significant correlations (p < 0.05) were found in 189 out of the 283 pairs of soil properties studied, PCA revealed that 6 main factors capture more variance than any individual original variable in the dataset (eigenvalues > 1), explaining 82.9% of the total variance. The amount of variance in each original variable captured by the extracted principal components (communalities) indicates that the six main factors explain > 90% of the variance in properties such as Corg, Nmic, Nmic/TN, Cmic, Nmin, Vp, and BD, and >80% of the variance for TN, P, Cmic/Corg, N0₃-N, and C/N. The six main factors, however, explain < 70% of the total variability in the properties of soil moist and NH₄-N (

Table 6. “Weights” and percentage of explained variance of the SQI of the six factors.

Soil Properties	Factors						Communalities
	1	2	3	4	5	6
TN	0.82	0.16	0.10	0.19	−0.26	−0.09	0.82
Corg	0.82	0.08	−0.10	0.22	0.44	−0.05	0.93
WHC	0.80	−0.18	0.08	0.30	0.08	0.13	0.79
Cact	0.73	0.30	0.00	0.15	−0.07	0.36	0.78
P	−0.61	−0.24	0.34	0.05	−0.52	0.02	0.81
Soil moist	0.54	0.11	0.05	0.01	−0.16	−0.50	0.58
NH4-N	0.49	0.41	0.22	−0.22	0.35	−0.14	0.65
Cmic/Corg	0.07	0.85	0.16	−0.02	−0.21	0.13	0.82
Nmic/Ntot	−0.07	0.78	0.10	0.04	0.32	−0.45	0.93
Nmic	0.29	0.75	0.15	0.12	0.06	−0.52	0.95
Cmic	0.56	0.74	0.11	0.12	0.13	0.04	0.90
Nmin	−0.01	0.39	0.87	−0.05	−0.12	−0.03	0.93
NO3-N	−0.18	0.28	0.85	0.01	−0.24	0.02	0.89
EC	0.29	−0.15	0.76	0.07	0.05	−0.14	0.70
VP	0.21	0.05	0.01	0.96	0.08	−0.04	0.98
BD	−0.21	−0.05	−0.01	−0.96	−0.08	0.04	0.98
C/N	−0.05	0.08	−0.15	0.12	0.88	0.12	0.84
Cact/Corg	−0.06	0.22	0.07	−0.13	−0.61	0.52	0.73

Bold text indicates the most significant coefficient (>0.7) for each Factor.

).

These factors were named based on the soil properties they most strongly correlated with. The first factor, “organic matter”, was strongly correlated (r² > 0.70) to TN, Corg, and Cact, as well as WHC. The second factor, “microbial biomass”, was associated with microbial biomass indicators like Cmic/Corg, Nmic/TN, Nmic, and Cmic. The third factor, “nutrients”, was strongly associated with Nmin, NO₃-N, and EC, which expresses the concentration of soil nutrients in the soil solution. The fourth factor, “compaction”, was strongly associated with the bulk density and porosity of the soil, which are key indexes of soil compaction. The sixth factor, “C/N ratio”, and seventh factor, “active carbon”, were determined solely by the C/N and Cact/Corg, respectively.

Each factor’s performance was evaluated across land uses using analysis of variance (ANOVA), which showed that all factors except for “active carbon” varied significantly by land use (

Table 7. Effect of land use on the factors score derived from principal component analysis.

	Factor Score					ANOVA
Factors	Forest	Olive Trees	Wheats	Double Cultiv.	Pasture	F	Sig.
Organic Matter	1.53 a	−0.80 d	−0.38 c	−0.46 c	0.10 b	121.24	0.00
Microbial Biomass	−0.36 c	−0.41 cd	0.43 b	−0.79 d	1.12 a	52.76	0.00
Nutrients	−0.47 c	−0.96 d	0.31 b	0.78 a	0.33 b	37.99	0.00
Compaction	0.23 b	0.04 b	0.99 a	−0.74 c	−0.51 c	34.41	0.00
C/N	0.63 a	−0.33 b	−0.59 b	−0.31 b	0.60 a	21.04	0.00
Active Carbon	−0.11 a	0.13 a	−0.01 a	0.02 a	−0.03 a	0.35	0.84

Values for each factor with the same letter do not differ significantly for p < 0.05 according to LSD post hoc test.

).

The ANOVA results show that, except for “active carbon”, all other soil quality factors exhibited significant variation across the different land uses (Table 7). The “organic matter” factor showed positive scores for forest and pasture soils but negative scores for other land uses, with the highest value in the forest and the lowest in crops, particularly in olive trees. The “microbial biomass” factor exhibited positive scores in pasture and crop soils, but negative scores were observed in forest, olive grove, and double-cropping systems, with pasture scoring the highest and double-cropping the lowest. In the same way, the “nutrients” factor exhibited higher scores for double-cropping, crops, and pasture. Forests and olive groves, on the contrary, recorded negative scores. The “compaction” factor revealed positive scores for crops, forests, and olive groves, but negative scores for double-cropping and pasture, with the highest score in crops and the lowest in double-cropping. The “C/N ratio” factor presented the highest positive scores in forest and pasture, with lower and negative scores in wheat and olive tree soils (Table 7).

Discriminant analysis (DA) was conducted using land use as the grouping variable to identify the factors that most significantly differentiated between land uses. DA generated four discriminant functions that explained 100% of the total variability (p < 0.001,

Table 8. Eigenvalues and percentages of the explained variance of the four functions when discriminating land uses by soil quality factors.

Functions	Eigenvalue	% of Variance	Cumulative %
1	7.903	75.9	75.9
2	1.237	11.9	87.8
3	0.924	8.9	96.7
4	0.347	3.3	100.0

).

The first function explained 75.9% of the total variance, followed by the second with 11.9%, the third with 8.9%, and the fourth with 3.3% (Table 8).

Y1 = 2.50(organic matter) + 0.41(soil nutrients) + 0.10(soil compaction) + 1.43(C/N) − 0.20(active carbon)

(1)

DA (

Table 9. Unstandardized coefficients of discrete functions of soil quality factors.

Factos	Functions
	1	2	3	4
1 Organic matter	2.504	−0.150	−0.117	0.385
2 Microbial biomass	0.416	1.069	0.756	−0.346
3 Nutrients	−0.460	0.903	−0.406	0.799
4 Compaction	0.104	−0.383	1.045	0.527
5 C/N	1.431	0.264	−0.221	−0.366
6 Active carbon	−0.203	−0.046	0.003	−0.082
(Constant)	0.000	0.000	0.000	0.000

), recognized “organic matter” and “C/N ratio” factors as the most influential in distinguishing between land uses (Equation (1)). The discrimination coefficient for the “organic matter” factor was approximately five times greater than that for the “soil nutrients” and “microbial biomass” factors, and over ten times higher than those for the “active carbon” and “soil compaction” factors. Similarly, the discrimination coefficient for the “C/N ratio” was roughly three times higher than that of the “nutrients “ and “microbial biomass” factors, and more than seven times higher than the coefficients for “active carbon” and “compaction”.

3.4. Identification of “Minimum Set of Soil Quality Indicators”

Discriminant analysis (DA) was conducted again on the “organic matter” factor, which includes seven soil properties, to identify the most influential properties in differentiating land uses. This analysis produced four functions explaining 100% of the total variability (p < 0.001,

Table 10. Eigenvalues and percentages of the explained variance of the four functions when discriminating land uses by organic matter factor.

Function	Eigenvalue	% of Variance	Cumulative %
1	13.646	84.2	84.2
2	2.078	12.8	97.0
3	0.313	1.9	99.0
4	0.169	1.0	100.0

), with the first function accounting for 84.2%, the second 12.8%, the third 1.9%, and the fourth 1.0% of the variance.

The linear discriminant function for the first factor is given in Equation (2) (

Table 11. Unstandardized coefficients of discrete functions of the organic matter factor.

SQI	Functions
	1	2	3	4
Corg	0.324	−0.098	−0.237	0.271
TN	2.000	−0.099	3.187	3.318
WHC	0.019	0.169	0.008	−0.039
moist	0.004	0.035	−0.036	−0.064
NH4-N	0.136	−0.021	0.312	−0.116
P	−0.234	0.043	0.031	0.089
Cact	−0.003	−0.010	0.000	0.004
(Constant)	−3.201	−6.596	−2.564	−5.163

). The analysis revealed that TN was the most important SQI for distinguishing land uses, with a discriminant coefficient several times larger than that of any other soil property (Equation (2)).

Y2 = 0.324(Corg) + 2.0(TN) + 0.19(WHC) + 0.004(moist) + 0.136(NH₄-N) − 0.234(P) − 0.003(Cact)

(2)

In conclusion, the results of PCA, DA, and the derived equations clearly show that the soil properties of TN and C/N ratio play a pivotal role in differentiating between land uses. These two indicators can be considered highly sensitive soil properties and are recommended for monitoring soil quality changes in relation to land use in the study area.

4. Discussion

In this study, the dataset used in the multivariate analyses incorporates the effects of both land use and season on the examined SQIs. Land use within the same climate and soil type influences soil function through management practices such as tillage, irrigation, fertilization, and biomass removal. Additionally, vegetation cover plays a role, as this determines the quantity and quality of plant residues entering the soil system. Although land use impacts individual soil quality indicators (SQIs), it has a more substantial effect on the overall set of indices (physical, chemical, and biological), grouping soil functions into distinct categories for each land use. For instance, soil functions in crop lands, while differing between crop types, are more pronounced compared to those in forests and pastures.

Seasonal variability of SQIs is a crucial factor in understanding soil function and has been investigated in the study area, specifically for soil microbial biomass properties [31]. In Mediterranean regions, the hot and dry summer conditions are likely to have a more pronounced impact on the temporal changes of many SQIs compared to other factors. For example, soil microbial biomass often shows a decline during the summer, followed by an increase in autumn as rainfall returns. This pattern is consistent across various land uses, despite differences in vegetation, management practices, or Corg levels in Mediterranean agroecosystems. However, the magnitude of these seasonal shifts—from spring to summer and summer to autumn—varies significantly depending on land use [31]. Furthermore, the timing of management practices, such as irrigation, soil cultivation, and fertilization, strongly influences the variability of SQIs, including nutrient availability, electrical conductivity (EC), and soil microbial biomass indices.

PCA highlighted NO₃-N and P as key indicators distinguishing soil functions across different land uses. The observed differences may be linked to soil management practices, such as the regular application of chemical fertilizers (e.g., 11-15-15 type) that increase inorganic phosphorus levels, as animal excretions in pastures that increase nitrate nitrogen, and the closed nutrient cycling in forests. Two other indicators, Cmic and Cact, representing labile organic carbon in soils [52,53,54,55], also distinguish soil functions among land uses. These indicators are influenced by management practices such as tillage and biomass removal in crops, animal excretions in pastures, and nutrient cycling in forests, which affect labile carbon pools in the soil. Crop residues serve as a crucial source of energy and nutrients for microbial proliferation, contributing to the formation of soil organic carbon. Labile/active organic carbon pools represent a small part of soil organic carbon but serve as sensitive indicators of soil biogeochemical processes under agricultural management [56] that must be further studied.

In the current study, six key soil quality factors were identified: organic matter, microbial biomass, nutrients, compaction, C/N ratio, and Cact/Corg ratio. Each of these factors plays a role in supporting one or more essential soil functions.

The organic matter factor, in particular, reflects both long-term and short-term changes associated with land use change [57]. Soil organic matter (SOM) underpins crucial ecosystem services, such as food production, climate regulation, water filtration, erosion control, nutrient cycling, and providing energy for soil organisms [58,59]. It is widely regarded as a vital indicator of soil quality for the Mediterranean agroecosystems [27,60,61,62]. SOM also plays a key role in enhancing the resilience and adaptability of soils to environmental pressures [1]. The loss of soil organic carbon, often observed during the conversion of natural ecosystems to agricultural systems [63,64], is associated with reduced inputs of organic materials, decreased natural protection of organic carbon due to tillage, shifts in soil moisture and temperature that accelerate decomposition rates, and increased soil erosion [65]. Tillage, in particular, involves the physical disruption of the upper soil layers, which reduces soil aggregation and influences the turnover of aggregates, thereby impacting the soil carbon balance [66]. Conservation tillage techniques, such as reduced tillage, have been shown to increase total organic carbon in the surface layer, promoting microaggregation, and enhancing aggregate stability [67]. These practices can be effective alternatives for improving soil quality by increasing organic matter in cultivated soils and have to be incorporated into Mediterranean agroecosystem management. Additionally, crop rotations that include legumes, along with the application of organic amendments like animal residues and organic waste, can enhance carbon storage in soils [68]. Finally, if a significant portion of the diminishing soil organic matter could be restored through appropriate management practices, it might even be possible to mitigate some of the annual increases in atmospheric CO₂ levels [69].

The microbial biomass factor governs ecological processes that drive carbon and nutrient cycles, making it a sensitive measure of soil management impacts. Soil microbial biomass has been extensively recognized as a critical soil quality indicator [70,71,72] and has been reported among the most important ecological indicators of soil quality in the Mediterranean ecosystem [30]. Microbial biomass serves as both a source of mobile nutrients and a key player in the cycling and transformation of organic matter and plant nutrients in the soil [73]. Understanding microbial properties—such as the quantity, diversity, and activity of microbial biomass—is crucial for gaining deeper insights into the factors that contribute to soil health [74]. As a property that can predict future shifts in the amount of total organic matter [75], soil microbial biomass monitoring is a valuable tool for understanding and anticipating long-term changes in soil conditions. Many management practices in agroecosystems have been linked to the reduction of soil organic matter, leading to declines in soil biological fertility and resilience [76]. This issue is particularly pronounced in the rainfed agricultural systems of Mediterranean climates, where high summer temperatures and the alternation of wet and dry soil conditions contribute to high annual mineralization rates of organic matter [77]. The variability of abiotic factors in the Mediterranean agroecosystems is more extreme compared to temperate regions [78], and thus the synchronization of soil fauna and flora activity with the dynamics of certain chemical soil properties, influenced by seasonal climate changes, is a defining characteristic of Mediterranean-type ecosystems [79].

The nutrients factor affects nutrient availability, while the compaction factor influences water retention, aeration, and soil physical, chemical, and biological properties.

The C/N and Cact/Corg factors, while unidimensional in factor analysis, represent complex and dynamics soil functions. The C/N ratio is a key indicator of the quality of organic substrates available for decomposition [80,81], while the Cact/Corg ratio reflects the mineralization dynamics of organic matter [41]. Together, these factors provide a comprehensive assessment of soil quality in Mediterranean agroecosystems.

The differentiation of the six quality factors based on land use reflects dynamic soil qualities [14] and assesses the impact of land use and management practices on soil quality. The organic matter and compaction factors have been previously recognized by other researchers [21,81]. In this study, four additional factors are identified: microbial biomass, nutrients, C/N, and Cact/Corg. Evaluating these soil quality factors identifies five of the six as significant for assessing changes in soil quality due to land use changes. The Cact/Corg factor appears less important, as it does not effectively express soil quality dynamics.

In general, soil quality factors exhibit similar behavior across different land uses, consistent with the dominant indicators that comprise them. The organic matter factor is significantly affected by the land use and associated soil management like cultivation practices. The microbial biomass factor is influenced by carbon and nitrogen incorporation into microbial biomass, while the nutrients factor is impacted by nitrogen availability (via fertilization or mineralization). Soil management practices affect the compaction factor, and the C/N factor is influenced by the quantity and composition of plant residues. Among these, the organic matter and C/N factors, which reflect the quantity and quality of soil organic matter, appear to be the most crucial determinants of soil quality in Mediterranean agroecosystems. Changes in soil quality, resulting from land use and management practices, are reflected in all components of each factor [82].

The impact of land use on soil function is closely linked to the intensity of land management. While different crops generally have a similar characteristic effect on overall soil function, they also exhibit differences due to variations in the intensity and type of cultivation practices associated with each crop. Specifically, in this study, land use influences the content of soil organic matter, its quality, and the active pools of organic carbon. It seems that the quality of organic matter and the characteristics of active carbon pools, rather than the overall concentration of soil organic matter, play a dominant role in determining nutrient availability in soils.

In this study, TN is highlighted as critical for determining shifts in soil quality within the soil organic matter factor. Its significance as a fundamental property for soil quality is noted in numerous studies [83,84,85] due to its incorporation of a large portion of the information related to interacting soil parameters. In this study, forest soils show the highest TN stocks, followed by grasslands and croplands, similar to findings in other studies [85,86]. TN was significantly correlated with soil moisture, clay, Corg, Cmic, and Cact, indicating its influence on both labile and stable forms of soil organic matter. TN is a SQI that incorporates soil organic matter dynamics, and furthermore, is a significant and direct contributor to plant nitrogen nutrition, even in agricultural contexts [87]. TN has been reported by Zhao et al. [88] for other types of climatic zones as a sensitive SQI among different land uses.

Soil C/N ratio emerged as a second crucial factor for assessing changes in soil quality. Its significance lies in its ability to reflect the dynamics of organic matter decomposition, which plays a pivotal role in overall soil quality. Soil C/N ratio has long been recognized as a key indicator of organic matter quality and nitrogen mineralization-immobilization processes [89]. Shifts in soil C/N stoichiometry are known to significantly influence carbon dynamics in agroecosystems [80]. Microorganisms use labile carbon as an energy source to produce extracellular enzymes, facilitating nitrogen extraction from soil organic matter (SOM) and leading to SOM mineralization [90,91]. Thus, the C/N ratio, though often underestimated, plays a fundamental role in regulating soil organic matter decomposition, indirectly impacting soil quality. The C/N ratio also serves as a common proxy for organic matter stability [92], offering insights into soil quality changes. While interpreting shifts in C/N ratios in bulk soils is complex, especially in response to land use or climate change, it is essential for understanding potential soil organic carbon (SOC) sequestration or losses, as well as nutrient cycling and availability in agroecosystems. This makes the C/N ratio a valuable indicator for tracking soil quality changes in agricultural systems.

From a set of 23 physical, chemical, and biological SQIs, this study identifies soil properties of total nitrogen and C/N ratio as sensitive key indicators, capturing most of the variability across all 23 SQIs among land uses. These two indicators provide valuable insights into soil quality changes resulting from land use changes or the application of specific management practices. The proposed indicators are sufficient for assessing long-term soil quality changes.

Although this research focuses on a specific region of the Mediterranean, the findings related to soil quality factors are likely applicable to other regions with Mediterranean-type climates, where moisture and temperature patterns are the primary drivers of fundamental soil functions. Many of the soil quality indicators used in this study demonstrate similar behavior in response to land use across regions with Mediterranean climates, such as California [93,94]. This study’s examination of how soil quality changes with land use change and cultivation practices in Mediterranean ecosystems underscores the need for further research. The significant role of the quantity and quality of soil organic matter, along with its components, in soil functions and nutrient availability highlights the importance of investigating organic matter dynamics using indicators that best reflect the parameters being studied. Additional research on sensitive indicators that can accurately predict changes in organic matter, such as active carbon and microbial biomass properties, would be particularly useful for the early detection of soil quality degradation.

As mentioned, the comparative evaluation of soil quality with changes in land use or different cultivation practices can be effectively conducted using a limited number of indicators. However, soil quality cannot be accurately assessed through one or two properties alone, as it is determined by a combination of physical, chemical, and biological characteristics. The cost and time required for comprehensive soil analysis make it impractical for farmers to regularly assess and monitor changes in soil quality. Therefore, using the selected indicators of TN and C/N that capture condensed information of a broader set of physical, chemical, and biological SQIs can serve as a practical tool for evaluating the sustainability of soil resources. These indicators allow producers and land managers to quickly and efficiently track changes in soil quality following land use change or the adoption of new farming practices, facilitating the re-evaluation of management strategies.

5. Conclusions

The concept of soil quality, which encompasses the holistic relationships and functions of physical, chemical, and biological soil properties, aligns with the sustainable management of non-renewable soil resources. In this study, the influence of land use on soil function and overall SQIs is emphasized, particularly under the unique conditions imposed by different land uses incorporating any seasonal variation of the SQIs in the Mediterranean agroecosystems. Agricultural use is shown to significantly affect soil parameters, differentiating them from the natural ecosystem of the forest, although considerable differences are observed even within agricultural systems. Cultivation practices appear to have a consistent impact on soil parameters, with olive trees, wheat, and double-cultivation systems grouped in a distinct manner, indicating that land cultivation affects soil function and differentiates it from other agricultural uses such as forest and pasture.

The study identifies five key factors that depict soil function: “organic matter”, “microbial biomass”, “nutrients”, the “C/N ratio”, and “compaction”. These factors influence one or more soil functions, explaining 82.9% of the total variability in the dataset of 23 physical, chemical, and biological SQIs, and can be used to comparatively reflect changes in soil quality due to land use change. Soil properties of TN and C/N ratio, which determine the quantity and quality of soil organic matter, emerge as particularly sensitive indicators of soil quality changes in Mediterranean agroecosystems. By monitoring TN and the C/N ratio, valuable insights into soil quality can be obtained, incorporating valuable evidence that can be derived by a bigger set of physical, chemical, and biological indicators. The comparison of soils based on these two indicators reveals the impact of land use changes and management practices on soil quality.

The proposed indicators, covering complex information about changes in soil quality, can serve as practical tools for assessing the sustainability of soil resources. Regular monitoring of TN and C/N soil properties enables producers and land management entities to rapidly and accurately determine shifts in soil quality following land use changes or the adoption of new cultivation practices, providing an opportunity to reassess and optimize management strategies.